Switching learning law for differential neural observer for biodegradation process

R. Fuentes, A. García, A. Cabrera, T. Poznyak, I. Chairez

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

Resumen

In this paper, it is presented a differential neural network supplied with a new learning law based on the sliding mode approach. The state observer is employed to estimate the dynamics states of degradation mathematical model, where the incomplete information and the limited on-line measure problems are considered. A new training method is applied in the learning algorithm is proposed to reconstruct Biomass, Organic Matter Recalcitrant concentrations and Volume of biological culture evolutions. This allows ensuring an upper bound for the weights time evolution. This new scheme gives the possibility to construct not only one adaptive process but a set of learning laws. The effectiveness of this algorithm is shown by numerical results.

Idioma originalInglés
Título de la publicación alojadaInternational Joint Conference on Neural Networks 2006, IJCNN '06
EditorialInstitute of Electrical and Electronics Engineers Inc.
Páginas4484-4490
Número de páginas7
ISBN (versión impresa)0780394909, 9780780394902
DOI
EstadoPublicada - 2006
EventoInternational Joint Conference on Neural Networks 2006, IJCNN '06 - Vancouver, BC, Canadá
Duración: 16 jul. 200621 jul. 2006

Serie de la publicación

NombreIEEE International Conference on Neural Networks - Conference Proceedings
ISSN (versión impresa)1098-7576

Conferencia

ConferenciaInternational Joint Conference on Neural Networks 2006, IJCNN '06
País/TerritorioCanadá
CiudadVancouver, BC
Período16/07/0621/07/06

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